import collections import math import os import random import sys import tarfile import time import zipfile from IPython import display from matplotlib import pyplot as plt import torch from torch import nn import torch.nn.functional as F import torchvision import torchvision.transforms as transforms # import mxnet as mx # from mxnet import autograd, gluon, image, init, nd # from mxnet.contrib import text # from mxnet.gluon import data as gdata, loss as gloss, nn, utils as gutils import numpy as np VOC_CLASSES = ['background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'potted plant', 'sheep', 'sofa', 'train', 'tv/monitor'] VOC_COLORMAP = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128]] # ###################### 3.2 ############################ def set_figsize(figsize=(3.5, 2.5)): use_svg_display() # 设置图的尺寸 plt.rcParams['figure.figsize'] = figsize def use_svg_display(): """Use svg format to display plot in jupyter""" display.set_matplotlib_formats('svg') def data_iter(batch_size, features, labels): num_examples = len(features) indices = list(range(num_examples)) random.shuffle(indices) # 样本的读取顺序是随机的 for i in range(0, num_examples, batch_size): j = torch.LongTensor(indices[i: min(i + batch_size, num_examples)]) # 最后一次可能不足一个batch yield features.index_select(0, j), labels.index_select(0, j) def linreg(X, w, b): return torch.mm(X, w) + b def squared_loss(y_hat, y): # 注意这里返回的是向量, 另外, pytorch里的MSELoss并没有除以 2 return ((y_hat - y.view(y_hat.size())) ** 2) / 2 def sgd(params, lr, batch_size): # 为了和原书保持一致，这里除以了batch_size，但是应该是不用除的，因为一般用PyTorch计算loss时就默认已经 # 沿batch维求了平均了。 for param in params: param.data -= lr * param.grad / batch_size # 注意这里更改param时用的param.data # ######################3##### 3.5 ############################# def get_fashion_mnist_labels(labels): text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat', 'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot'] return [text_labels[int(i)] for i in labels] def show_fashion_mnist(images, labels): use_svg_display() # 这里的_表示我们忽略（不使用）的变量 _, figs = plt.subplots(1, len(images), figsize=(12, 12)) for f, img, lbl in zip(figs, images, labels): f.imshow(img.view((28, 28)).numpy()) f.set_title(lbl) f.axes.get_xaxis().set_visible(False) f.axes.get_yaxis().set_visible(False) # plt.show() # 5.6 修改 # def load_data_fashion_mnist(batch_size, root='~/Datasets/FashionMNIST'): # """Download the fashion mnist dataset and then load into memory.""" # transform = transforms.ToTensor() # mnist_train = torchvision.datasets.FashionMNIST(root=root, train=True, download=True, transform=transform) # mnist_test = torchvision.datasets.FashionMNIST(root=root, train=False, download=True, transform=transform) # if sys.platform.startswith('win'): # num_workers = 0 # 0表示不用额外的进程来加速读取数据 # else: # num_workers = 4 # train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers) # test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=num_workers) # return train_iter, test_iter # ########################### 3.6 ############################### # (3.13节修改) # def evaluate_accuracy(data_iter, net): # acc_sum, n = 0.0, 0 # for X, y in data_iter: # acc_sum += (net(X).argmax(dim=1) == y).float().sum().item() # n += y.shape[0] # return acc_sum / n def train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, params=None, lr=None, optimizer=None): for epoch in range(num_epochs): train_l_sum, train_acc_sum, n = 0.0, 0.0, 0 for X, y in train_iter: y_hat = net(X) l = loss(y_hat, y).sum() # 梯度清零 if optimizer is not None: optimizer.zero_grad() elif params is not None and params[0].grad is not None: for param in params: param.grad.data.zero_() l.backward() if optimizer is None: sgd(params, lr, batch_size) else: optimizer.step() # “softmax回归的简洁实现”一节将用到 train_l_sum += l.item() train_acc_sum += (y_hat.argmax(dim=1) == y).sum().item() n += y.shape[0] test_acc = evaluate_accuracy(test_iter, net) print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f' % (epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc)) # ########################### 3.7 #####################################3 class FlattenLayer(torch.nn.Module): def __init__(self): super(FlattenLayer, self).__init__() def forward(self, x): # x shape: (batch, *, *, ...) return x.view(x.shape[0], -1) # ########################### 3.11 ############################### def semilogy(x_vals, y_vals, x_label, y_label, x2_vals=None, y2_vals=None, legend=None, figsize=(3.5, 2.5)): set_figsize(figsize) plt.xlabel(x_label) plt.ylabel(y_label) plt.semilogy(x_vals, y_vals) if x2_vals and y2_vals: plt.semilogy(x2_vals, y2_vals, linestyle=':') plt.legend(legend) # plt.show() # ############################# 3.13 ############################## # 5.5 修改 # def evaluate_accuracy(data_iter, net): # acc_sum, n = 0.0, 0 # for X, y in data_iter: # if isinstance(net, torch.nn.Module): # net.eval() # 评估模式, 这会关闭dropout # acc_sum += (net(X).argmax(dim=1) == y).float().sum().item() # net.train() # 改回训练模式 # else: # 自定义的模型 # if('is_training' in net.__code__.co_varnames): # 如果有is_training这个参数 # # 将is_training设置成False # acc_sum += (net(X, is_training=False).argmax(dim=1) == y).float().sum().item() # else: # acc_sum += (net(X).argmax(dim=1) == y).float().sum().item() # n += y.shape[0] # return acc_sum / n # ########################### 5.1 ######################### def corr2d(X, K): h, w = K.shape Y = torch.zeros((X.shape[0] - h + 1, X.shape[1] - w + 1)) for i in range(Y.shape[0]): for j in range(Y.shape[1]): Y[i, j] = (X[i: i + h, j: j + w] * K).sum() return Y # ############################ 5.5 ######################### def evaluate_accuracy(data_iter, net, device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')): acc_sum, n = 0.0, 0 with torch.no_grad(): for X, y in data_iter: if isinstance(net, torch.nn.Module): net.eval() # 评估模式, 这会关闭dropout acc_sum += (net(X.to(device)).argmax(dim=1) == y.to(device)).float().sum().cpu().item() net.train() # 改回训练模式 else: # 自定义的模型, 3.13节之后不会用到, 不考虑GPU if('is_training' in net.__code__.co_varnames): # 如果有is_training这个参数 # 将is_training设置成False acc_sum += (net(X, is_training=False).argmax(dim=1) == y).float().sum().item() else: acc_sum += (net(X).argmax(dim=1) == y).float().sum().item() n += y.shape[0] return acc_sum / n def train_ch5(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs): net = net.to(device) print("training on ", device) loss = torch.nn.CrossEntropyLoss() batch_count = 0 for epoch in range(num_epochs): train_l_sum, train_acc_sum, n, start = 0.0, 0.0, 0, time.time() for X, y in train_iter: X = X.to(device) y = y.to(device) y_hat = net(X) l = loss(y_hat, y) optimizer.zero_grad() l.backward() optimizer.step() train_l_sum += l.cpu().item() train_acc_sum += (y_hat.argmax(dim=1) == y).sum().cpu().item() n += y.shape[0] batch_count += 1 test_acc = evaluate_accuracy(test_iter, net) print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, time %.1f sec' % (epoch + 1, train_l_sum / batch_count, train_acc_sum / n, test_acc, time.time() - start)) # ########################## 5.6 #########################3 def load_data_fashion_mnist(batch_size, resize=None, root='~/Datasets/FashionMNIST'): """Download the fashion mnist dataset and then load into memory.""" trans = [] if resize: trans.append(torchvision.transforms.Resize(size=resize)) trans.append(torchvision.transforms.ToTensor()) transform = torchvision.transforms.Compose(trans) mnist_train = torchvision.datasets.FashionMNIST(root=root, train=True, download=True, transform=transform) mnist_test = torchvision.datasets.FashionMNIST(root=root, train=False, download=True, transform=transform) if sys.platform.startswith('win'): num_workers = 0 # 0表示不用额外的进程来加速读取数据 else: num_workers = 4 train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers) test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=num_workers) return train_iter, test_iter ############################# 5.8 ############################## class GlobalAvgPool2d(nn.Module): # 全局平均池化层可通过将池化窗口形状设置成输入的高和宽实现 def __init__(self): super(GlobalAvgPool2d, self).__init__() def forward(self, x): return F.avg_pool2d(x, kernel_size=x.size()[2:]) # ########################### 5.11 ################################ class Residual(nn.Module): def __init__(self, in_channels, out_channels, use_1x1conv=False, stride=1): super(Residual, self).__init__() self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, stride=stride) self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1) if use_1x1conv: self.conv3 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride) else: self.conv3 = None self.bn1 = nn.BatchNorm2d(out_channels) self.bn2 = nn.BatchNorm2d(out_channels) def forward(self, X): Y = F.relu(self.bn1(self.conv1(X))) Y = self.bn2(self.conv2(Y)) if self.conv3: X = self.conv3(X) return F.relu(Y + X) def resnet_block(in_channels, out_channels, num_residuals, first_block=False): if first_block: assert in_channels == out_channels # 第一个模块的通道数同输入通道数一致 blk = [] for i in range(num_residuals): if i == 0 and not first_block: blk.append(Residual(in_channels, out_channels, use_1x1conv=True, stride=2)) else: blk.append(Residual(out_channels, out_channels)) return nn.Sequential(*blk) def resnet18(output=10, in_channels=3): net = nn.Sequential( nn.Conv2d(in_channels, 64, kernel_size=7, stride=2, padding=3), nn.BatchNorm2d(64), nn.ReLU(), nn.MaxPool2d(kernel_size=3, stride=2, padding=1)) net.add_module("resnet_block1", resnet_block(64, 64, 2, first_block=True)) net.add_module("resnet_block2", resnet_block(64, 128, 2)) net.add_module("resnet_block3", resnet_block(128, 256, 2)) net.add_module("resnet_block4", resnet_block(256, 512, 2)) net.add_module("global_avg_pool", GlobalAvgPool2d()) # GlobalAvgPool2d的输出: (Batch, 512, 1, 1) net.add_module("fc", nn.Sequential(FlattenLayer(), nn.Linear(512, output))) return net # ############################## 6.3 ##################################3 def load_data_jay_lyrics(): """加载周杰伦歌词数据集""" with zipfile.ZipFile('../../data/jaychou_lyrics.txt.zip') as zin: with zin.open('jaychou_lyrics.txt') as f: corpus_chars = f.read().decode('utf-8') corpus_chars = corpus_chars.replace('\n', ' ').replace('\r', ' ') corpus_chars = corpus_chars[0:10000] idx_to_char = list(set(corpus_chars)) char_to_idx = dict([(char, i) for i, char in enumerate(idx_to_char)]) vocab_size = len(char_to_idx) corpus_indices = [char_to_idx[char] for char in corpus_chars] return corpus_indices, char_to_idx, idx_to_char, vocab_size def data_iter_random(corpus_indices, batch_size, num_steps, device=None): # 减1是因为输出的索引x是相应输入的索引y加1 num_examples = (len(corpus_indices) - 1) // num_steps epoch_size = num_examples // batch_size example_indices = list(range(num_examples)) random.shuffle(example_indices) # 返回从pos开始的长为num_steps的序列 def _data(pos): return corpus_indices[pos: pos + num_steps] if device is None: device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') for i in range(epoch_size): # 每次读取batch_size个随机样本 i = i * batch_size batch_indices = example_indices[i: i + batch_size] X = [_data(j * num_steps) for j in batch_indices] Y = [_data(j * num_steps + 1) for j in batch_indices] yield torch.tensor(X, dtype=torch.float32, device=device), torch.tensor(Y, dtype=torch.float32, device=device) def data_iter_consecutive(corpus_indices, batch_size, num_steps, device=None): if device is None: device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') corpus_indices = torch.tensor(corpus_indices, dtype=torch.float32, device=device) data_len = len(corpus_indices) batch_len = data_len // batch_size indices = corpus_indices[0: batch_size*batch_len].view(batch_size, batch_len) epoch_size = (batch_len - 1) // num_steps for i in range(epoch_size): i = i * num_steps X = indices[:, i: i + num_steps] Y = indices[:, i + 1: i + num_steps + 1] yield X, Y # ###################################### 6.4 ###################################### def one_hot(x, n_class, dtype=torch.float32): # X shape: (batch), output shape: (batch, n_class) x = x.long() res = torch.zeros(x.shape[0], n_class, dtype=dtype, device=x.device) res.scatter_(1, x.view(-1, 1), 1) return res def to_onehot(X, n_class): # X shape: (batch, seq_len), output: seq_len elements of (batch, n_class) return [one_hot(X[:, i], n_class) for i in range(X.shape[1])] def predict_rnn(prefix, num_chars, rnn, params, init_rnn_state, num_hiddens, vocab_size, device, idx_to_char, char_to_idx): state = init_rnn_state(1, num_hiddens, device) output = [char_to_idx[prefix[0]]] for t in range(num_chars + len(prefix) - 1): # 将上一时间步的输出作为当前时间步的输入 X = to_onehot(torch.tensor([[output[-1]]], device=device), vocab_size) # 计算输出和更新隐藏状态 (Y, state) = rnn(X, state, params) # 下一个时间步的输入是prefix里的字符或者当前的最佳预测字符 if t < len(prefix) - 1: output.append(char_to_idx[prefix[t + 1]]) else: output.append(int(Y[0].argmax(dim=1).item())) return ''.join([idx_to_char[i] for i in output]) def grad_clipping(params, theta, device): norm = torch.tensor([0.0], device=device) for param in params: norm += (param.grad.data ** 2).sum() norm = norm.sqrt().item() if norm > theta: for param in params: param.grad.data *= (theta / norm) def train_and_predict_rnn(rnn, get_params, init_rnn_state, num_hiddens, vocab_size, device, corpus_indices, idx_to_char, char_to_idx, is_random_iter, num_epochs, num_steps, lr, clipping_theta, batch_size, pred_period, pred_len, prefixes): if is_random_iter: data_iter_fn = data_iter_random else: data_iter_fn = data_iter_consecutive params = get_params() loss = nn.CrossEntropyLoss() for epoch in range(num_epochs): if not is_random_iter: # 如使用相邻采样，在epoch开始时初始化隐藏状态 state = init_rnn_state(batch_size, num_hiddens, device) l_sum, n, start = 0.0, 0, time.time() data_iter = data_iter_fn(corpus_indices, batch_size, num_steps, device) for X, Y in data_iter: if is_random_iter: # 如使用随机采样，在每个小批量更新前初始化隐藏状态 state = init_rnn_state(batch_size, num_hiddens, device) else: # 否则需要使用detach函数从计算图分离隐藏状态, 这是为了 # 使模型参数的梯度计算只依赖一次迭代读取的小批量序列(防止梯度计算开销太大) for s in state: s.detach_() inputs = to_onehot(X, vocab_size) # outputs有num_steps个形状为(batch_size, vocab_size)的矩阵 (outputs, state) = rnn(inputs, state, params) # 拼接之后形状为(num_steps * batch_size, vocab_size) outputs = torch.cat(outputs, dim=0) # Y的形状是(batch_size, num_steps)，转置后再变成长度为 # batch * num_steps 的向量，这样跟输出的行一一对应 y = torch.transpose(Y, 0, 1).contiguous().view(-1) # 使用交叉熵损失计算平均分类误差 l = loss(outputs, y.long()) # 梯度清0 if params[0].grad is not None: for param in params: param.grad.data.zero_() l.backward() grad_clipping(params, clipping_theta, device) # 裁剪梯度 sgd(params, lr, 1) # 因为误差已经取过均值，梯度不用再做平均 l_sum += l.item() * y.shape[0] n += y.shape[0] if (epoch + 1) % pred_period == 0: print('epoch %d, perplexity %f, time %.2f sec' % ( epoch + 1, math.exp(l_sum / n), time.time() - start)) for prefix in prefixes: print(' -', predict_rnn(prefix, pred_len, rnn, params, init_rnn_state, num_hiddens, vocab_size, device, idx_to_char, char_to_idx)) # ################################### 6.5 ################################################ class RNNModel(nn.Module): def __init__(self, rnn_layer, vocab_size): super(RNNModel, self).__init__() self.rnn = rnn_layer self.hidden_size = rnn_layer.hidden_size * (2 if rnn_layer.bidirectional else 1) self.vocab_size = vocab_size self.dense = nn.Linear(self.hidden_size, vocab_size) self.state = None def forward(self, inputs, state): # inputs: (batch, seq_len) # 获取one-hot向量表示 X = to_onehot(inputs, self.vocab_size) # X是个list Y, self.state = self.rnn(torch.stack(X), state) # 全连接层会首先将Y的形状变成(num_steps * batch_size, num_hiddens)，它的输出 # 形状为(num_steps * batch_size, vocab_size) output = self.dense(Y.view(-1, Y.shape[-1])) return output, self.state def predict_rnn_pytorch(prefix, num_chars, model, vocab_size, device, idx_to_char, char_to_idx): state = None output = [char_to_idx[prefix[0]]] # output会记录prefix加上输出 for t in range(num_chars + len(prefix) - 1): X = torch.tensor([output[-1]], device=device).view(1, 1) if state is not None: if isinstance(state, tuple): # LSTM, state:(h, c) state = (state[0].to(device), state[1].to(device)) else: state = state.to(device) (Y, state) = model(X, state) # 前向计算不需要传入模型参数 if t < len(prefix) - 1: output.append(char_to_idx[prefix[t + 1]]) else: output.append(int(Y.argmax(dim=1).item())) return ''.join([idx_to_char[i] for i in output]) def train_and_predict_rnn_pytorch(model, num_hiddens, vocab_size, device, corpus_indices, idx_to_char, char_to_idx, num_epochs, num_steps, lr, clipping_theta, batch_size, pred_period, pred_len, prefixes): loss = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=lr) model.to(device) state = None for epoch in range(num_epochs): l_sum, n, start = 0.0, 0, time.time() data_iter = data_iter_consecutive(corpus_indices, batch_size, num_steps, device) # 相邻采样 for X, Y in data_iter: if state is not None: # 使用detach函数从计算图分离隐藏状态, 这是为了 # 使模型参数的梯度计算只依赖一次迭代读取的小批量序列(防止梯度计算开销太大) if isinstance (state, tuple): # LSTM, state:(h, c) state = (state[0].detach(), state[1].detach()) else: state = state.detach() (output, state) = model(X, state) # output: 形状为(num_steps * batch_size, vocab_size) # Y的形状是(batch_size, num_steps)，转置后再变成长度为 # batch * num_steps 的向量，这样跟输出的行一一对应 y = torch.transpose(Y, 0, 1).contiguous().view(-1) l = loss(output, y.long()) optimizer.zero_grad() l.backward() # 梯度裁剪 grad_clipping(model.parameters(), clipping_theta, device) optimizer.step() l_sum += l.item() * y.shape[0] n += y.shape[0] try: perplexity = math.exp(l_sum / n) except OverflowError: perplexity = float('inf') if (epoch + 1) % pred_period == 0: print('epoch %d, perplexity %f, time %.2f sec' % ( epoch + 1, perplexity, time.time() - start)) for prefix in prefixes: print(' -', predict_rnn_pytorch( prefix, pred_len, model, vocab_size, device, idx_to_char, char_to_idx)) # ######################################## 7.2 ############################################### def train_2d(trainer): x1, x2, s1, s2 = -5, -2, 0, 0 # s1和s2是自变量状态，本章后续几节会使用 results = [(x1, x2)] for i in range(20): x1, x2, s1, s2 = trainer(x1, x2, s1, s2) results.append((x1, x2)) print('epoch %d, x1 %f, x2 %f' % (i + 1, x1, x2)) return results def show_trace_2d(f, results): plt.plot(*zip(*results), '-o', color='#ff7f0e') x1, x2 = np.meshgrid(np.arange(-5.5, 1.0, 0.1), np.arange(-3.0, 1.0, 0.1)) plt.contour(x1, x2, f(x1, x2), colors='#1f77b4') plt.xlabel('x1') plt.ylabel('x2') # ######################################## 7.3 ############################################### def get_data_ch7(): data = np.genfromtxt('../../data/airfoil_self_noise.dat', delimiter='\t') data = (data - data.mean(axis=0)) / data.std(axis=0) return torch.tensor(data[:1500, :-1], dtype=torch.float32), \ torch.tensor(data[:1500, -1], dtype=torch.float32) # 前1500个样本(每个样本5个特征) def train_ch7(optimizer_fn, states, hyperparams, features, labels, batch_size=10, num_epochs=2): # 初始化模型 net, loss = linreg, squared_loss w = torch.nn.Parameter(torch.tensor(np.random.normal(0, 0.01, size=(features.shape[1], 1)), dtype=torch.float32), requires_grad=True) b = torch.nn.Parameter(torch.zeros(1, dtype=torch.float32), requires_grad=True) def eval_loss(): return loss(net(features, w, b), labels).mean().item() ls = [eval_loss()] data_iter = torch.utils.data.DataLoader( torch.utils.data.TensorDataset(features, labels), batch_size, shuffle=True) for _ in range(num_epochs): start = time.time() for batch_i, (X, y) in enumerate(data_iter): l = loss(net(X, w, b), y).mean() # 使用平均损失 # 梯度清零 if w.grad is not None: w.grad.data.zero_() b.grad.data.zero_() l.backward() optimizer_fn([w, b], states, hyperparams) # 迭代模型参数 if (batch_i + 1) * batch_size % 100 == 0: ls.append(eval_loss()) # 每100个样本记录下当前训练误差 # 打印结果和作图 print('loss: %f, %f sec per epoch' % (ls[-1], time.time() - start)) set_figsize() plt.plot(np.linspace(0, num_epochs, len(ls)), ls) plt.xlabel('epoch') plt.ylabel('loss') # 本函数与原书不同的是这里第一个参数优化器函数而不是优化器的名字 # 例如: optimizer_fn=torch.optim.SGD, optimizer_hyperparams={"lr": 0.05} def train_pytorch_ch7(optimizer_fn, optimizer_hyperparams, features, labels, batch_size=10, num_epochs=2): # 初始化模型 net = nn.Sequential( nn.Linear(features.shape[-1], 1) ) loss = nn.MSELoss() optimizer = optimizer_fn(net.parameters(), **optimizer_hyperparams) def eval_loss(): return loss(net(features).view(-1), labels).item() / 2 ls = [eval_loss()] data_iter = torch.utils.data.DataLoader( torch.utils.data.TensorDataset(features, labels), batch_size, shuffle=True) for _ in range(num_epochs): start = time.time() for batch_i, (X, y) in enumerate(data_iter): # 除以2是为了和train_ch7保持一致, 因为squared_loss中除了2 l = loss(net(X).view(-1), y) / 2 optimizer.zero_grad() l.backward() optimizer.step() if (batch_i + 1) * batch_size % 100 == 0: ls.append(eval_loss()) # 打印结果和作图 print('loss: %f, %f sec per epoch' % (ls[-1], time.time() - start)) set_figsize() plt.plot(np.linspace(0, num_epochs, len(ls)), ls) plt.xlabel('epoch') plt.ylabel('loss') ############################## 8.3 ################################## class Benchmark(): def __init__(self, prefix=None): self.prefix = prefix + ' ' if prefix else '' def __enter__(self): self.start = time.time() def __exit__(self, *args): print('%stime: %.4f sec' % (self.prefix, time.time() - self.start)) # ########################### 9.1 ######################################## def show_images(imgs, num_rows, num_cols, scale=2): figsize = (num_cols * scale, num_rows * scale) _, axes = plt.subplots(num_rows, num_cols, figsize=figsize) for i in range(num_rows): for j in range(num_cols): axes[i][j].imshow(imgs[i * num_cols + j]) axes[i][j].axes.get_xaxis().set_visible(False) axes[i][j].axes.get_yaxis().set_visible(False) return axes def train(train_iter, test_iter, net, loss, optimizer, device, num_epochs): net = net.to(device) print("training on ", device) batch_count = 0 for epoch in range(num_epochs): train_l_sum, train_acc_sum, n, start = 0.0, 0.0, 0, time.time() for X, y in train_iter: X = X.to(device) y = y.to(device) y_hat = net(X) l = loss(y_hat, y) optimizer.zero_grad() l.backward() optimizer.step() train_l_sum += l.cpu().item() train_acc_sum += (y_hat.argmax(dim=1) == y).sum().cpu().item() n += y.shape[0] batch_count += 1 test_acc = evaluate_accuracy(test_iter, net) print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, time %.1f sec' % (epoch + 1, train_l_sum / batch_count, train_acc_sum / n, test_acc, time.time() - start)) ############################## 9.3 ##################### def bbox_to_rect(bbox, color): # 将边界框(左上x, 左上y, 右下x, 右下y)格式转换成matplotlib格式： # ((左上x, 左上y), 宽, 高) return d2l.plt.Rectangle( xy=(bbox[0], bbox[1]), width=bbox[2]-bbox[0], height=bbox[3]-bbox[1], fill=False, edgecolor=color, linewidth=2)